Current models (e.g., HRNetv2 [1]}, HRNet+OCR [2]}) perform multi-scale feature extraction by sharing feature maps across their different levels (scales), i.e., broadcasting context information at various resolutions.
Contrary to multi-scale models and to capture high-resolution feature maps, PSP-Net [3]} performs pooling operations at multiple grid scales.
Simultaneously, DeepLabv3+ [4]} and CasiNet [5]} use Atrous Spatial Pyramid Pooling (ASPP) [6]}, [7]} (i.e., several sparse filters) to modify the filters' size instead of the images' size [8]}.
Later experiments showed that there are still limitations to get global features [9]}.
Moreover, the introduced dilated convolutions bring heavy computation complexity and a memory footprint, thus limiting many applications' usage.
